[Summary of the best evidence for the energy and protein intake targets and calculation in critically ill patients].
Bibliographic record
Abstract
OBJECTIVE: To evaluate and summarize the best evidence of energy and protein intake targets and calculation in adult critically ill patients, and to provide evidence-based basis for critical nutrition management. METHODS: Evidence related to energy and protein intake targets and calculation of adult critically ill patients, including guideline, expert consensus, systematic review and evidence summary, were systematically searched in PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase, Cochrane Library, UpToDate, BMJ Best Practice, Joanna Briggs Institute (JBI), Web of Science, SinoMed, Medive, China National Knowledge Infrastructure, Wanfang database, VIP database, Guidelines International Network (GIN), National Institute for Health and Care Excellence (NICE), National Guideline Clearinghouse (NGC), Registered Nurses Association of Ontario (RNAO), and Society of Critical Care Medicine (SCCM) from January 2012 to June 2022. Two researchers independently evaluated the quality of the included literatures using the JBI Evidence-based Health Care Center evaluation tool and the Appraisal of Clinical Practice Guidelines for Research and Evaluation II (AGREE II), extracted and summarized the best evidence for the nutritional intake goal and calculation of adult critically ill patients, and described the evidence. RESULTS: A total of 18 literatures were included, including 5 clinical guidelines, 8 expert consensus, 3 systematic reviews and 2 evidence summaries. After literature quality evaluation, 18 articles were all enrolled. The evidence was summarized from the four aspects, including energy target calculation method, dose body weight, energy and protein intake target, and calculation method, 24 pieces of the best evidence were finally formed. CONCLUSIONS: The best evidence of energy and protein intake targets and calculation for critically ill patients was summarized based on evidence-based. Clinical medical staff can choose indirect calorimetry to calculate energy goals when equipment is available. Patient's height, body weight should be recorded accurately, dose body weight can be determined by body mass index (BMI). Meanwhile, blood urea nitrogen (BUN) loss, fat-free body weight, simple formulas and other methods should be used to continuously evaluate and adjust protein intake targets, to achieve the purpose of optimizing intensive nutrition support.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".